TianyuCodings writes about JevHarness, a system where an LLM authors a task-specific harness for Jev (a lightweight judgment model from TypeSafe). The harness converts task observations into features, constructs Jev questions and criteria, and combines structured answers into actions. Once the harness is frozen, execution runs only the harness code and Jev calls without the authoring LLM on every decision. An optional GEPA evolution loop refines the harness using rewards and complete execution traces.
- Pokemon eval: 5 reflection rounds improved win rate from 25% (3/12) to 75% (9/12); the round-3 candidate was selected as best
- Selected harness median full decision: 568 ms (P95 657 ms); individual Jev request median: 269 ms (P95 348 ms)
- Installable as a Claude Code plugin or Codex skill; the skill guides the agent to clarify task inputs, legal actions, and success criteria before building
- Requires Python 3.11+; functional Python nodes need a macOS native sandbox and fail closed if unavailable
- The archived website and recorded-call inspection require no model credentials
- Pokemon eval: 5 reflection rounds improved win rate from 25% (3/12) to 75% (9/12); the round-3 candidate was selected as best
- Selected harness median full decision: 568 ms (P95 657 ms); individual Jev request median: 269 ms (P95 348 ms)
- Installable as a Claude Code plugin or Codex skill; the skill guides the agent to clarify task inputs, legal actions, and success criteria before building
- Requires Python 3.11+; functional Python nodes need a macOS native sandbox and fail closed if unavailable
- The archived website and recorded-call inspection require no model credentials
jev-social is an open-source tool that pairs the Jev model (from Typesafe AI) with the socai browser CLI to perform grounded social media research on Instagram, TikTok, and LinkedIn. Jev makes sequential decisions about which operation to perform next (search, open a profile, read comments, download media), while socai executes each chosen command in a real Chrome instance and returns the result before Jev makes the next decision.
- Packaged as a Codex plugin and installable via `npx` or the cross-agent Skills CLI
- Each step's choice, confidence, command, and elapsed time are stored for auditability
- Enforces read-only and login-gate boundaries; unsupported or low-confidence decisions are rejected rather than executed
- A recorded Instagram run produced four source-linked records in ~64 seconds
- Runs loopback-only on localhost:8766 and requires an OpenRouter API key with Jev access
- Packaged as a Codex plugin and installable via `npx` or the cross-agent Skills CLI
- Each step's choice, confidence, command, and elapsed time are stored for auditability
- Enforces read-only and login-gate boundaries; unsupported or low-confidence decisions are rejected rather than executed
- A recorded Instagram run produced four source-linked records in ~64 seconds
- Runs loopback-only on localhost:8766 and requires an OpenRouter API key with Jev access
Dev Agrawal writes about Stanley, a command-line tool that routes natural-language requests about code changes to deterministic workflows. Each workflow gathers bounded evidence from a Git diff or log and asks the TypeSafe Jev model fixed-choice questions about it; the tool's own code applies thresholds to make decisions, never delegating that step to the model. When no installed workflow covers a request, Stanley falls back to the Pi coding agent and then queues a background job to draft a new workflow for that kind of request, which the user can promote into a permanent, agent-free path.
- Ten built-in workflows: check, review, test gaps, security, performance, compatibility, summarization, code search, failure triage, and comment triage
- An empty findings list is explicitly not an approval; every report includes a `notChecked` field
- Repository workflows under `.stanley/workflows/` run in-process with full user privileges and cannot declare CLI flags
- Agent-written workflows are quarantined until explicitly promoted via `--promote-candidate`; the agent cannot self-activate
- 0.1.0 is not yet on npm; the old `jev-code` placeholder package (0.0.1) does nothing
- 110 stars, 6 forks, single contributor, MIT licensed, 100% TypeScript, no releases published
- Ten built-in workflows: check, review, test gaps, security, performance, compatibility, summarization, code search, failure triage, and comment triage
- An empty findings list is explicitly not an approval; every report includes a `notChecked` field
- Repository workflows under `.stanley/workflows/` run in-process with full user privileges and cannot declare CLI flags
- Agent-written workflows are quarantined until explicitly promoted via `--promote-candidate`; the agent cannot self-activate
- 0.1.0 is not yet on npm; the old `jev-code` placeholder package (0.0.1) does nothing
- 110 stars, 6 forks, single contributor, MIT licensed, 100% TypeScript, no releases published
Mark Marosi writes about decider, a family of models fine-tuned from Qwen3.5 that produce typed decisions (choice, score, boolean) in a single forward pass without text generation, returning calibrated probability distributions over user-defined options. The project is an open reproduction of TypeSafe AI's "System One" model class (Jev), released in sizes from 0.8B to 35B mixture-of-experts with 3B active parameters.
- The schema cache stores K/V states for repeated question prefixes, achieving up to 19x speedup on large option sets by running only the state per request
- v10 adds calibration-aware RL on live MiniWoB++ browser tasks and exact games, lifting browser accuracy from 83% to 93% and halving the belief gap
- TypeSafe's SDKs work unchanged by pointing TYPESAFE_BASE_URL at the decider server
- The 35B model outperforms the 2B on 93 of 95 regression tasks but costs 3-4x per decision and lacks the RL stage
- Co-developed with Claude (Anthropic) as a listed co-author on commits
- The schema cache stores K/V states for repeated question prefixes, achieving up to 19x speedup on large option sets by running only the state per request
- v10 adds calibration-aware RL on live MiniWoB++ browser tasks and exact games, lifting browser accuracy from 83% to 93% and halving the belief gap
- TypeSafe's SDKs work unchanged by pointing TYPESAFE_BASE_URL at the decider server
- The 35B model outperforms the 2B on 93 of 95 regression tasks but costs 3-4x per decision and lacks the RL stage
- Co-developed with Claude (Anthropic) as a listed co-author on commits
Usenet-Rewind is a search platform that indexes Usenet newsgroup archives dating from 1981 to the present, covering early tech support discussions, scientific and academic exchanges, hobbyist communities, and firsthand accounts of internet history. Operated by Erie Data Systems, LLC, the service currently holds approximately 1.09 billion messages with a retention period exceeding 45 years.
- 12.37 million new messages added recently
- Offers API access for programmatic search
- Supports advanced filtering by title, body, author, message ID, newsgroup, and date range
- Provides a browsable timeline of Usenet topics, people, and events by decade
- 12.37 million new messages added recently
- Offers API access for programmatic search
- Supports advanced filtering by title, body, author, message ID, newsgroup, and date range
- Provides a browsable timeline of Usenet topics, people, and events by decade
Python 0.9.1 is the historical first public beta release of the Python programming language. It was uploaded and distributed by its creator, Guido van Rossum, to the Usenet newsgroup alt.sources on February 19, 1991
Michael Degano serves as food and beverage general manager at Kachina Cantina, a modern cantina in Denver's Lower Downtown neighborhood. A New Jersey native from a large Italian family, he began his career bartending in San Francisco before spending 16 years at Kimpton Hotels and Restaurants, where he managed properties in Seattle and Washington, D.C., and served as director of operations. He joined Sage Restaurant Concepts in 2017 and now oversees both Kachina Cantina and Poka Lola Social Club.
w3cj writes about jev-chat, a tool-calling chat bot that routes user requests to real tools using Jev, a non-generative classifier from TypeSafe, with no LLM writing any output. Every value on screen was either typed by the user or returned by a tool, so the assistant cannot invent a fact. The system supports weather, unit conversion, Wikipedia lookups, recipes, web search, Todoist, and Home Assistant via MCP servers, with an inspector pane exposing every decision and probability for each turn.
- Jev answers only two question types — Choice and Noul — and never produces text; all reply wording is templated in code
- Pre-processing handles spell-check (cspell + compromise) and resolves short follow-ups like "what about Boston?" by swapping in the new value
- Multi-step tools (Wikipedia, web search) chain multiple Jev requests: pick topic, then article, then the exact line that answers
- Confidence-gated routing shows two buttons when the top tools are close rather than guessing
- The repo is a proof of concept; the author will not accept PRs for new features
- English only; no compound requests or multi-step reasoning supported
- Jev answers only two question types — Choice and Noul — and never produces text; all reply wording is templated in code
- Pre-processing handles spell-check (cspell + compromise) and resolves short follow-ups like "what about Boston?" by swapping in the new value
- Multi-step tools (Wikipedia, web search) chain multiple Jev requests: pick topic, then article, then the exact line that answers
- Confidence-gated routing shows two buttons when the top tools are close rather than guessing
- The repo is a proof of concept; the author will not accept PRs for new features
- English only; no compound requests or multi-step reasoning supported
jeff is a self-hosted drop-in replacement for TypeSafe's jev System One API, powered by the 400M-parameter GLiFormer-large-v1 model. It serves `choice`, `score`, and `noul` classification questions over a compatible wire format, so existing applications using the official `typesafe-sdk` can point at it by changing a single base URL. Deployment targets include GPU (L4, A10G) and CPU (ONNX Runtime with int8 quantization) via Modal, with ~50 req/s per container throughput on L4. Benchmarks on 1,600 labeled items show it costs roughly a quarter of jev per million requests but trails significantly on reasoning-heavy tasks like irony and reading comprehension.
- `JEFF_ISOLATE=nouls` (default) gives separate encoder passes per noul question to reduce cross-question interference; choice and score questions share a pass unless set to `all`
- Default temperature of 3.2 calibrates noul probabilities; setting it to 1 makes `score` output match the weighted average of displayed probabilities
- A smaller `gliformer-base-v1` variant with `JEFF_NOUL_MODE=single` is recommended for faster local iteration
- `JEFF_ISOLATE=nouls` (default) gives separate encoder passes per noul question to reduce cross-question interference; choice and score questions share a pass unless set to `all`
- Default temperature of 3.2 calibrates noul probabilities; setting it to 1 makes `score` output match the weighted average of displayed probabilities
- A smaller `gliformer-base-v1` variant with `JEFF_NOUL_MODE=single` is recommended for faster local iteration
Gregory Gibson writes about applying the Linux kernel's tool-generated content guidance to his vibe-coded Morse decoder app, CW Inspector. He used OpenAI Codex to build the app, then subjected it to the kernel's five transparency rules: name the tool, preserve inputs, keep the prompt trail, record exactly what the tool changed, and make the generated code prove itself. The process exposed a critical flaw — the decoder confidently produced a wrong answer (reading TTT E as SE) with zero errors, demonstrating that a clean run doesn't guarantee correctness.
- The original algorithm treated the shortest 55% of keyed pulses as dots, which broke on dash-heavy messages; the fix looks for a large ratio between short and long pulse clusters before recording dot duration.
- The author deliberately withheld the actual WAV file from Codex, providing only metadata and the expected message, to create an independent acceptance test rather than letting the model optimize against the exact sample it would later decode.
- The kernel's guidance applies only when a tool generates something substantial (functions, files, fixes, translations), not for spelling corrections, autocomplete, or variable renames.
- The original algorithm treated the shortest 55% of keyed pulses as dots, which broke on dash-heavy messages; the fix looks for a large ratio between short and long pulse clusters before recording dot duration.
- The author deliberately withheld the actual WAV file from Codex, providing only metadata and the expected message, to create an independent acceptance test rather than letting the model optimize against the exact sample it would later decode.
- The kernel's guidance applies only when a tool generates something substantial (functions, files, fixes, translations), not for spelling corrections, autocomplete, or variable renames.
